Quantile Mixture and Probability Mixture Models in a Multi-Model Approach to Flood Frequency Analysis

A Mixture-Model Approach to the Analysis of Survival Data

In this paper, we study a mixture model for survival data where covariates may innuence both the incidence probabilities and their conditional latency distributions. The data may include the exactly observed, the right-censored, and the interval-censored failure times. We apply the EM algorithm to nd the maximum likelihood estimate. We also carry out a Markov chain Monte Carlo algorithm for Bay...

A Mixture Approach to Universal Model Selectionolivier

Using Regression based Control Limits and Probability Mixture Models for Monitoring Customer Behavior

In order to achieve the maximum flexibility in adaptation to ever changing customer’s expectations in customer relationship management, appropriate measures of customer behavior should be continually monitored. To this end, control charts adjusted for buyer’s/visitor’s prior intention to repurchase or visit again are suitable means taking into account the heterogeneity across customers. In the ...

Model Selection for Mixture Models Using Perfect Sample

We have considered a perfect sample method for model selection of finite mixture models with either known (fixed) or unknown number of components which can be applied in the most general setting with assumptions on the relation between the rival models and the true distribution. It is, both, one or neither to be well-specified or mis-specified, they may be nested or non-nested. We consider mixt...

Imaging radiotracer model parameters in PET: a mixture analysis approach

Two methodologies for fitting radiotracer models on a pixel-wise basis to PET data are considered. The first method does parameter optimization for each pixel considered as a separate region of interest. The second method also does pixel-wise analysis but incorporates an additive mixture representation to account for heterogeneity effects induced by instrumental and biological blurring. Several...